class_docstr: Base video anomalib dataset class.
  
  Args:
      task (str): Task type, either 'classification' or 'segmentation'
      transform (A.Compose): Albumentations Compose object describing the transforms that are applied to the inputs.
      clip_length_in_frames (int): Number of video frames in each clip.
      frames_between_clips (int): Number of frames between each consecutive video clip.
      target_frame (VideoTargetFrame): Specifies the target frame in the video clip, used for ground truth retrieval
code: def _select_targets(self, item):
      if self.target_frame == VideoTargetFrame.FIRST:
          idx = 0
      elif self.target_frame == VideoTargetFrame.LAST:
          idx = -1
      elif self.target_frame == VideoTargetFrame.MID:
          idx = int(self.clip_length_in_frames / 2)
      else:
          raise ValueError(
              f"Unknown video target frame: {self.target_frame}"
          )
  
      if item.get("mask") is not None:
          item["mask"] = item["mask"][idx, ...]
      if item.get("boxes") is not None:
          item["boxes"] = item["boxes"][idx]
      if item.get("label") is not None:
          item["label"] = item["label"][idx]
      if item.get("original_image") is not None:
          item["original_image"] = item["original_image"][idx]
      if item.get("frames") is not None:
          item["frames"] = item["frames"][idx]
      return item
  
imports: from __future__ import annotations
  from abc import ABC
  from enum import Enum
  from typing import Callable
  import albumentations
  import torch
  from pandas import DataFrame
  from torch import Tensor
  from anomalib.data.base.datamodule import AnomalibDataModule
  from anomalib.data.base.dataset import AnomalibDataset
  from anomalib.data.task_type import TaskType
  from anomalib.data.utils import ValSplitMode, masks_to_boxes
  from anomalib.data.utils.video import ClipsIndexer
label_desc: Use image node_id 6 for calling the AnomalibVideoDataset obj's underlying member method code with example usage: obj._select_targets(item) and returns: item